Comprehensive interview preparation for the world’s hardest companies to get into, targeting full-stack engineering roles with deep AI/ML infrastructure expertise.
| Company | Focus Areas | Comp Range (Senior) |
|---|
| Anthropic | LLM inference, GPU serving, concurrency, safety | ~$550K |
| Google | DS&A at scale, system design, Googleyness, TPU/ML | ~$400-650K |
| Google DeepMind | ML research + engineering, JAX, AlphaFold, Gemini | ~$400-750K |
| OpenAI | API infrastructure, ML systems, mission alignment | ~$450-600K |
| Meta | Planetary scale, AI-enabled coding round, LLaMA | ~$450-700K |
| Apple | Privacy-first ML, on-device inference, product excellence | ~$400-650K |
| NVIDIA | GPU/CUDA programming, parallel computing, AI infra | ~$400-750K |
| Moonshot AI | Long-context LLMs, NLP research, Chinese AI frontier | Competitive + equity |
| Company | Focus Areas | Comp Range (Senior) |
|---|
| Netflix | Distributed systems, streaming, culture, recommendations | ~$350-500K (cash) |
| Amazon | Leadership Principles, services architecture, AWS/ML | ~$350-450K (Y1) |
| Databricks | Most selective non-FAANG, Lakehouse, Spark, Delta Lake | ~$400-700K |
| Stripe | No leetcode, bug bash, payments infra, financial correctness | ~$400-650K |
| Palantir | Decomposition, data platforms, entity modeling, AIP | ~$350-500K |
| Company | Focus Areas | Comp Range (Senior) |
|---|
| Jane Street | OCaml, probability, low-latency, market making | ~$600K-1.5M |
| Two Sigma | DP-heavy coding, data infra, quant reasoning | ~$500K-1M+ |
| Citadel / Citadel Securities | Hardest algo rounds, C++, low-latency matching | ~$500K-1.5M+ |
| HRT (Hudson River Trading) | Nanosecond latency, C++, FPGA, kernel bypass | ~$700K-2M+ |
| Renaissance Technologies | Most exclusive firm on Earth, invite-only, PhD bar | ~$1M-5M+ |
| Company | Focus Areas | Comp Range (Senior) |
|---|
| SpaceX | 7-9 rounds, mission-critical software, Starlink | ~$300-500K + equity |
| Tier | Companies | What Makes It Hard |
|---|
| Near-impossible | Renaissance Technologies | Invite-only, ~300 employees, PhD required |
| Extreme | HRT, SpaceX, Citadel, Jane Street | Tiny headcount, world-class algorithmic bar |
| Very Hard | DeepMind, Anthropic, Databricks, Two Sigma | Research-grade bar, highly selective |
| Hard | Google, Meta, OpenAI, Stripe, NVIDIA, Moonshot | Structured but demanding, massive applicant pools |
| Hard | Netflix, Amazon, Apple, Palantir | Strong bar with unique cultural/domain requirements |
| Round Type | Google | Meta | OpenAI | Netflix | Amazon | Stripe | NVIDIA | Jane Street | Citadel | SpaceX |
|---|
| Coding | 2-3 | 2 (1 AI-enabled) | 2 | 1 | 2 | 1 (practical) | 1-2 | 1-2 | 2 | 3-4 |
| System Design | 1 | 1 | 1 | 1-2 | 1 | 1 (payments) | 1 | 1 | 1 | 1 |
| Behavioral | 1 | 1 | 1 | 1-2 (culture) | 4 (all rounds) | 1 | 1 | 1 | 1 | 1 |
| Unique Round | — | AI coding | — | Culture deep dive | Bar Raiser | Bug Bash + Integration | GPU/CUDA | Probability/Math | — | Take-home (4hr) |
| Total Rounds | 5-7 | 5-6 | 5-6 | 4-5 | 5-6 | 5 | 4-6 | 5-7 | 4-5 | 7-9 |
Defend Your System is the course’s interview module (iv.01, Pass 11): a question bank answered against the system you built, with the ADRs, load reports, and postmortems that back each answer.
- Start with shared concepts — Build the foundation that applies everywhere
- Pick your target companies — Focus on 2-3 at a time
- Study company-specific patterns — Each company has unique interview styles
- Practice with code — Don’t just read; implement every code sample
- Time yourself — Most rounds are 45-60 minutes with 5-10 min for questions
- Cross-reference — Many concepts overlap (concurrency appears at Anthropic, Google, NVIDIA, and all quant firms)